Toxicological insights of Spike fragments SARS-CoV-2 by exposure environment: A threat to aquatic health?

The Spike protein (S protein) is a critical component in the infection of the new coronavirus (SARS-CoV-2). The objective of this work was to evaluate whether peptides from S protein could cause negative impact in the aquatic animals. The aquatic toxicity of SARS-CoV-2 Spike protein peptides derivatives has been evaluated in tadpoles (n = 50 tadpoles/5 replicates of 10 animals) from species Physalaemus cuvieri (Leptodactylidae). After synthesis, purification, and characterization of peptides (PSDP2001, PSDP2002, PSDP2003) an aquatic contamination has been simulated with these peptides during 24 h of exposure in two concentrations (100 and 500 ng/mL). The control group ("C") was composed of tadpoles kept in polyethylene containers containing de-chlorinated water. Oxidative stress, antioxidant biomarkers and AChE activity were assessed. In both concentrations, PSPD2002 and PSPD2003 increased catalase and superoxide dismutase antioxidants enzymes activities, as well as oxidative stress (nitrite levels, hydrogen peroxide and reactive oxygen species). All three peptides also increased acetylcholinesterase activity in the highest concentration. These peptides showed molecular interactions in silico with acetylcholinesterase and antioxidant enzymes. Aquatic particle contamination of SARS-CoV-2 has cholinesterasic effect in P. cuvieri tadpoles. These findings indicate that the COVID-19 can constitute environmental impact or biological damage potential.

Converging Multidimensional Sensor and Machine Learning Toward High-Throughput and Biorecognition Element-Free Multidetermination of Extracellular Vesicle Biomarkers.

Extracellular vesicles (EVs) are a frontier class of circulating biomarkers for the diagnosis and prognosis of different diseases. These lipid structures afford various biomarkers such as the concentrations of the EVs (CV) themselves and carried proteins (CP). However, simple, high-throughput, and accurate determination of these targets remains a key challenge. Herein, we address the simultaneous monitoring of CV and CP from a single impedance spectrum without using recognizing elements by combining a multidimensional sensor and machine learning models. This multidetermination is essential for diagnostic accuracy because of the heterogeneous composition of EVs and their molecular cargoes both within the tumor itself and among patients. Pencil HB cores acting as electric double-layer capacitors were integrated into a scalable microfluidic device, whereas supervised models provided accurate predictions, even from a small number of training samples. User-friendly measurements were performed with sample-to-answer data processing on a smartphone. This new platform further showed the highest throughput when compared with the techniques described in the literature to quantify EVs biomarkers. Our results shed light on a method with the ability to determine multiple EVs biomarkers in a simple and fast way, providing a promising platform to translate biofluid-based diagnostics into clinical workflows.